"""
质量评分模型训练框架（预留）
"""
import logging
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime
import json
from pathlib import Path
import numpy as np

logger = logging.getLogger(__name__)


@dataclass
class TrainingDataset:
    """训练数据集接口"""
    samples: List[Dict[str, Any]]
    features: Optional[np.ndarray] = None
    labels: Optional[np.ndarray] = None
    metadata: Dict[str, Any] = None
    
    def __post_init__(self):
        """初始化后处理"""
        if self.metadata is None:
            self.metadata = {
                'created_at': datetime.utcnow().isoformat(),
                'sample_count': len(self.samples),
                'version': '1.0.0'
            }
    
    def split(self, train_ratio: float = 0.8) -> Tuple['TrainingDataset', 'TrainingDataset']:
        """
        分割训练集和验证集
        
        Args:
            train_ratio: 训练集比例
            
        Returns:
            训练集和验证集
        """
        split_idx = int(len(self.samples) * train_ratio)
        
        train_samples = self.samples[:split_idx]
        val_samples = self.samples[split_idx:]
        
        train_dataset = TrainingDataset(
            samples=train_samples,
            features=self.features[:split_idx] if self.features is not None else None,
            labels=self.labels[:split_idx] if self.labels is not None else None,
            metadata={**self.metadata, 'split': 'train'}
        )
        
        val_dataset = TrainingDataset(
            samples=val_samples,
            features=self.features[split_idx:] if self.features is not None else None,
            labels=self.labels[split_idx:] if self.labels is not None else None,
            metadata={**self.metadata, 'split': 'validation'}
        )
        
        return train_dataset, val_dataset


class QualityModelTrainer:
    """质量评分模型训练器（预留实现）"""
    
    def __init__(self, model_type: str = "gradient_boosting"):
        """
        初始化训练器
        
        Args:
            model_type: 模型类型
        """
        self.model_type = model_type
        self.model = None
        self.feature_extractor = FeatureExtractor()
        self.model_config = self._get_default_config()
        self.training_history = []
        
        logger.info(f"Initialized QualityModelTrainer with model_type: {model_type}")
        logger.warning("This is a placeholder implementation. Actual training logic to be implemented.")
    
    def _get_default_config(self) -> Dict[str, Any]:
        """获取默认配置"""
        return {
            'learning_rate': 0.01,
            'epochs': 100,
            'batch_size': 32,
            'early_stopping_patience': 10,
            'optimizer': 'adam',
            'loss_function': 'mse',
            'metrics': ['mae', 'r2_score'],
            'random_state': 42
        }
    
    async def train_model(
        self,
        dataset: TrainingDataset,
        validation_dataset: Optional[TrainingDataset] = None,
        **kwargs
    ) -> Dict[str, Any]:
        """
        训练模型（占位符实现）
        
        Args:
            dataset: 训练数据集
            validation_dataset: 验证数据集
            **kwargs: 额外参数
            
        Returns:
            训练结果
        """
        logger.info("Starting model training (placeholder)")
        
        # 更新配置
        self.model_config.update(kwargs)
        
        # 提取特征（占位符）
        if dataset.features is None:
            logger.info("Extracting features from training data...")
            dataset.features = await self.feature_extractor.extract_features(dataset.samples)
        
        # 模拟训练过程
        training_result = {
            'status': 'completed',
            'model_type': self.model_type,
            'training_samples': len(dataset.samples),
            'validation_samples': len(validation_dataset.samples) if validation_dataset else 0,
            'config': self.model_config,
            'metrics': {
                'train_loss': 0.05,  # 模拟值
                'val_loss': 0.08,     # 模拟值
                'train_mae': 2.3,     # 模拟值
                'val_mae': 3.1,       # 模拟值
                'r2_score': 0.85      # 模拟值
            },
            'training_time': '0:05:00',
            'timestamp': datetime.utcnow().isoformat(),
            'note': 'This is a placeholder training result. Actual implementation pending.'
        }
        
        # 记录训练历史
        self.training_history.append(training_result)
        
        logger.info(f"Training completed (placeholder): {training_result}")
        
        return training_result
    
    async def save_model(self, model_path: str) -> bool:
        """
        保存模型（占位符实现）
        
        Args:
            model_path: 模型保存路径
            
        Returns:
            是否保存成功
        """
        try:
            model_path = Path(model_path)
            model_path.parent.mkdir(parents=True, exist_ok=True)
            
            # 保存模型元数据（占位符）
            model_metadata = {
                'model_type': self.model_type,
                'config': self.model_config,
                'training_history': self.training_history,
                'saved_at': datetime.utcnow().isoformat(),
                'version': '1.0.0',
                'note': 'Placeholder model file. Actual model weights not saved.'
            }
            
            with open(model_path, 'w', encoding='utf-8') as f:
                json.dump(model_metadata, f, indent=2, ensure_ascii=False)
            
            logger.info(f"Model saved to {model_path} (placeholder)")
            return True
            
        except Exception as e:
            logger.error(f"Failed to save model: {e}")
            return False
    
    async def load_model(self, model_path: str) -> bool:
        """
        加载模型（占位符实现）
        
        Args:
            model_path: 模型文件路径
            
        Returns:
            是否加载成功
        """
        try:
            model_path = Path(model_path)
            
            if not model_path.exists():
                logger.error(f"Model file not found: {model_path}")
                return False
            
            with open(model_path, 'r', encoding='utf-8') as f:
                model_metadata = json.load(f)
            
            self.model_type = model_metadata.get('model_type', 'gradient_boosting')
            self.model_config = model_metadata.get('config', self._get_default_config())
            self.training_history = model_metadata.get('training_history', [])
            
            logger.info(f"Model loaded from {model_path} (placeholder)")
            logger.warning("Actual model weights not loaded. This is a placeholder.")
            
            return True
            
        except Exception as e:
            logger.error(f"Failed to load model: {e}")
            return False
    
    async def predict(self, features: np.ndarray) -> np.ndarray:
        """
        预测（占位符实现）
        
        Args:
            features: 特征向量
            
        Returns:
            预测结果
        """
        logger.warning("Using placeholder prediction. Actual model not implemented.")
        
        # 返回随机预测值（占位符）
        predictions = np.random.uniform(60, 90, size=features.shape[0])
        
        return predictions
    
    async def evaluate(
        self,
        dataset: TrainingDataset,
        metrics: Optional[List[str]] = None
    ) -> Dict[str, float]:
        """
        评估模型（占位符实现）
        
        Args:
            dataset: 评估数据集
            metrics: 评估指标列表
            
        Returns:
            评估结果
        """
        if metrics is None:
            metrics = self.model_config.get('metrics', ['mae', 'r2_score'])
        
        logger.warning("Using placeholder evaluation. Actual model not implemented.")
        
        # 返回模拟评估结果
        evaluation_result = {
            'mae': np.random.uniform(2, 5),
            'mse': np.random.uniform(10, 30),
            'r2_score': np.random.uniform(0.7, 0.9),
            'accuracy': np.random.uniform(0.8, 0.95),
            'sample_count': len(dataset.samples)
        }
        
        # 只返回请求的指标
        result = {k: v for k, v in evaluation_result.items() if k in metrics or k == 'sample_count'}
        
        logger.info(f"Evaluation completed (placeholder): {result}")
        
        return result


class FeatureExtractor:
    """特征提取器（预留实现）"""
    
    def __init__(self):
        """初始化特征提取器"""
        self.feature_dims = 50  # 特征维度
        logger.info("Initialized FeatureExtractor (placeholder)")
    
    async def extract_features(self, samples: List[Dict[str, Any]]) -> np.ndarray:
        """
        提取特征（占位符实现）
        
        Args:
            samples: 样本列表
            
        Returns:
            特征矩阵
        """
        logger.info(f"Extracting features from {len(samples)} samples (placeholder)")
        
        # 生成随机特征（占位符）
        features = np.random.randn(len(samples), self.feature_dims)
        
        # 实际实现中，这里应该：
        # 1. 提取文本统计特征（长度、词汇多样性等）
        # 2. 提取语言特征（句子结构、语法复杂度等）
        # 3. 提取语义特征（主题相关性、情感倾向等）
        # 4. 使用预训练模型提取深度特征
        
        return features
    
    def get_feature_names(self) -> List[str]:
        """
        获取特征名称列表
        
        Returns:
            特征名称列表
        """
        # 占位符特征名称
        return [f"feature_{i}" for i in range(self.feature_dims)]


class ModelConfig:
    """模型配置模板"""
    
    @staticmethod
    def get_config_template() -> Dict[str, Any]:
        """
        获取配置模板
        
        Returns:
            配置模板
        """
        return {
            'model': {
                'type': 'gradient_boosting',
                'parameters': {
                    'n_estimators': 100,
                    'max_depth': 5,
                    'learning_rate': 0.01,
                    'subsample': 0.8
                }
            },
            'training': {
                'epochs': 100,
                'batch_size': 32,
                'validation_split': 0.2,
                'early_stopping': {
                    'enabled': True,
                    'patience': 10,
                    'monitor': 'val_loss',
                    'mode': 'min'
                }
            },
            'optimization': {
                'optimizer': 'adam',
                'learning_rate': 0.001,
                'loss_function': 'mse',
                'metrics': ['mae', 'mse', 'r2_score']
            },
            'preprocessing': {
                'normalize_features': True,
                'remove_outliers': True,
                'outlier_threshold': 3.0
            },
            'output': {
                'save_best_model': True,
                'save_training_history': True,
                'model_dir': './models',
                'log_dir': './logs'
            }
        }
    
    @staticmethod
    def save_config(config: Dict[str, Any], path: str):
        """保存配置到文件"""
        with open(path, 'w', encoding='utf-8') as f:
            json.dump(config, f, indent=2, ensure_ascii=False)
    
    @staticmethod
    def load_config(path: str) -> Dict[str, Any]:
        """从文件加载配置"""
        with open(path, 'r', encoding='utf-8') as f:
            return json.load(f)